Signal and Information Processing for Sensing Systems, Institute for Bioengineering of Catalonia (IBEC), The Barcelona Institute of Science and Technology, Baldiri Reixac 10-12, 08028, Barcelona, Spain.
Signal and Information Processing for Sensing Systems, Institute for Bioengineering of Catalonia (IBEC), The Barcelona Institute of Science and Technology, Baldiri Reixac 10-12, 08028, Barcelona, Spain; Department of Electronics and Biomedical Engineering, University of Barcelona, Marti I Franqués 1, 08028 Barcelona, Spain.
Clin Chim Acta. 2022 Feb 1;526:6-13. doi: 10.1016/j.cca.2021.12.019. Epub 2021 Dec 23.
In this work, breath samples from clinically stable bronchiectasis patients with and without bronchial infections by Pseudomonas Aeruginosa- PA) were collected and chemically analysed to determine if they have clinical value in the monitoring of these patients.
A cohort was recruited inviting bronchiectasis patients (25) and controls (9). Among the former group, 12 members were suffering PA infection. Breath samples were collected in Tedlar bags and analyzed by e-nose and Gas Chromatography-Mass Spectrometry (GC-MS). The obtained data were analyzed by chemometric methods to determine their discriminant power in regards to their health condition. Results were evaluated with blind samples.
Breath analysis by electronic nose successfully separated the three groups with an overall classification rate of 84% for the three-class classification problem. The best discrimination was obtained between control and bronchiectasis with PA infection samples 100% (CI: 84-100%) on external validation and the results were confirmed by permutation tests. The discrimination analysis by GC-MS provided good results but did not reach proper statistical significance after a permutation test.
Breath sample analysis by electronic nose followed by proper predictive models successfully differentiated between control, Bronchiectasis and Bronchiectasis PA samples.
本研究采集了临床稳定的支气管扩张症患者（有无铜绿假单胞菌[PA]引起的支气管感染）的呼出气样本，并进行化学分析，以确定其在这些患者监测中的临床价值。
本研究招募了一组支气管扩张症患者（25 例）和对照组（9 例）。在前者中，有 12 名患者感染了 PA。使用 Tedlar 袋采集呼出气样本，并用电子鼻和气相色谱-质谱联用仪（GC-MS）进行分析。采用化学计量学方法对获得的数据进行分析，以确定其在健康状况方面的判别能力。结果采用盲法进行评估。
电子鼻分析成功地将三组患者区分开来，在三分类问题中总体分类率为 84%。在外部验证中，对对照组和 PA 感染支气管扩张症患者样本的最佳区分率为 100%（置信区间：84%-100%），且经置换检验得到确认。GC-MS 的判别分析提供了良好的结果，但经置换检验后未达到适当的统计学意义。
电子鼻分析呼出气样本并结合适当的预测模型可成功区分对照组、支气管扩张症和 PA 感染支气管扩张症患者。
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